International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

This paper presents an experimental study related to the optimization of cutting parameters in roughing turning of AISI 1045 steel under flooded conditions. The aim is to find a suitable combination of cutting parameters (cutting speed, depth of cut and feed rate) that minimize specific cutting energy and maximize material removal rate. The machining experiments were performed based on the Taguchi L27 full-factorial orthogonal array and response surface methodology (RSM) has been used to obtain the regression model for the specific cutting energy and material removal rate. Analysis of variance (ANOVA) was used to find out the significance of each cutting parameter. Finally, the developed models were interfaced with an artificial bee colony (ABC) to determine the optimal set of cutting parameters.

Controlling the heat during cutting operations are an important factor in improving the quality of the product and performance of the machining. For controlling the heat, there are a lot of methods. One of this is Vortex Tube Cooling System. In this study, the Vortex tube cooling system was used to control heat during the turning operation. In the design of experiment, cutting speed, cooling temperature, feed rate, and cutting tool corner radius were selected as input parameters. Surface roughness and cutting force was selected as output parameters. The input and output parameters were modeled by using Artificial Neural Network, the results of experiment were optimized.